Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy

© 2015 IEEE.Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results

Development and Testing of a High Resolution PET Detector for Prostate Imaging

According to the American Cancer Society one in six men will be diagnosed with prostate cancer in their lifetime. Current methods for screening of prostate cancer including various PSA blood tests, as well as the digital rectal exam, are unreliability while current imaging modalities clinically employed (US, CT, MRI) are unable to localize intraprostatic cancer(s). Consequently, diagnosis via core needle biopsy is problematic and a game of chance at best. Therefore, in response to new radiopharmaceuticals applicable to both internal and external prostate cancer visualization and localization, novel prostate specific nuclear medical imagers are being developed.;The first prototype of a compact prostate specific PET detector utilizing silicon photomultiplier (SiPM) technology has been developed and tested at West Virginia University. The compact detector is proposed as an endorectal probe placed proximally to the rectal wall/prostate interface and operating in coincidence with one or more externally mounted large area gamma detectors or in tandem with a clinical whole body PET scanner. To ensure high reconstruction resolution, the scintillation array of the compact detector will be coupled to SiPMs on both axial ends in a dual ended readout approach. Such an approach allows for the extraction of continuous depth of interaction (DOI) information thus minimizing the effects of parallax error and providing nearly isotropic and uniform spatial resolution throughout the entire detector field of view (FOV).;Two compact DOI based prototype detectors were developed and tested. While both utilize pixelated LYSO scintillation crystal arrays, the first has a crystal pitch of 1.0 mm and is coupled to SensL SiPMs, while the second has a crystal pitch of 0.7mm and is coupled to Hamamatsu SiPMs. Initial proof of concept studies were preformed using the SensL based detector while more extensive and systematic studies were preformed using the Hamamatsu based detector. Ultimately, when averaged over all crystals and all depths the Hamamatsu based detector achieved a depth of interaction resolution of 0.78+/-0.09 mm FWHM and an energy resolution of 13.2+/-0.7 % FWHM. Validation studies with regards to the efficacy of incorporating DOI information extracted from a small compact DOI based PET detector module into image reconstruction algorithms were also preformed

Phosphotyrosine Signaling Analysis in Human Tumors Is Confounded by Systemic Ischemia-Driven Artifacts and Intra-Specimen Heterogeneity

Tumor protein phosphorylation analysis may provide insight into intracellular signaling networks underlying tumor behavior, revealing diagnostic, prognostic or therapeutic information. Human tumors collected by The Cancer Genome Atlas program potentially offer the opportunity to characterize activated networks driving tumor progression, in parallel with the genetic and transcriptional landscape already documented for these tumors. However, a critical question is whether cellular signaling networks can be reliably analyzed in surgical specimens, where freezing delays and spatial sampling disparities may potentially obscure physiologic signaling. To quantify the extent of these effects, we analyzed the stability of phosphotyrosine (pTyr) sites in ovarian and colon tumors collected under conditions of controlled ischemia and in the context of defined intratumoral sampling. Cold-ischemia produced a rapid, unpredictable, and widespread impact on tumor pTyr networks within 5 minutes of resection, altering up to 50% of pTyr sites by more than 2-fold. Effects on adhesion and migration, inflammatory response, proliferation, and stress response pathways were recapitulated in both ovarian and colon tumors. In addition, sampling of spatially distinct colon tumor biopsies revealed pTyr differences as dramatic as those associated with ischemic times, despite uniform protein expression profiles. Moreover, intratumoral spatial heterogeneity and pTyr dynamic response to ischemia varied dramatically between tumors collected from different patients. Overall, these findings reveal unforeseen phosphorylation complexity, thereby increasing the difficulty of extracting physiologically relevant pTyr signaling networks from archived tissue specimens. In light of this data, prospective tumor pTyr analysis will require appropriate sampling and collection protocols to preserve in vivo signaling features.National Institutes of Health (U.S.) (Grant U24 CA159988

Laser Based Mid-Infrared Spectroscopic Imaging – Exploring a Novel Method for Application in Cancer Diagnosis

A number of biomedical studies have shown that mid-infrared spectroscopic images can provide both morphological and biochemical information that can be used for the diagnosis of cancer. Whilst this technique has shown great potential it has yet to be employed by the medical profession. By replacing the conventional broadband thermal source employed in modern FTIR spectrometers with high-brightness, broadly tuneable laser based sources (QCLs and OPGs) we aim to solve one of the main obstacles to the transfer of this technology to the medical arena; namely poor signal to noise ratios at high spatial resolutions and short image acquisition times. In this thesis we take the first steps towards developing the optimum experimental configuration, the data processing algorithms and the spectroscopic image contrast and enhancement methods needed to utilise these high intensity laser based sources. We show that a QCL system is better suited to providing numerical absorbance values (biochemical information) than an OPG system primarily due to the QCL pulse stability. We also discuss practical protocols for the application of spectroscopic imaging to cancer diagnosis and present our spectroscopic imaging results from our laser based spectroscopic imaging experiments of oesophageal cancer tissue

Applications of advanced spectroscopic imaging to biological tissues

The objectives of this research were to develop experimental approaches that can be applied to classify different stages of malignancy in routine formalin-fixed and paraffin-embedded tissues and to optimise the imaging approaches using novel implementations. It is hoped that the approach developed in this research may be applied for early cancer diagnostics in clinical settings in the future in order to increase cancer survival rates. Infrared spectroscopic imaging has recently shown to have great potential as a powerful method for the spatial visualization of biological tissues. This spectroscopic technique does not require sample labelling because its chemical specificity allows the differentiation of biocomponents to be achieved based on their chemical structures. Experiments were performed on 3-µm thick prostate and colon tissues that were deposited on 2 mm-calcium fluoride (CaF2) which were subsequently deparaffinised. The samples were measured under IR microscopes, in both transmission and attenuated total reflection (ATR) mode. In transmission, thermo-spectroscopic imaging of the prostate samples was first carried out to investigate the potential of thermography to complement the information obtained from IR spectral. Spectroscopic imaging has made the acquisition of chemical map of a sample possible within a short time span since this approach facilitates the simultaneous acquisition of thousands of spatially resolved infrared spectra. Spectral differences in the lipid region (3000 -2800 cm-1) were identified between cancer and benign regions within prostate tissues. The governing spectral band for classification was anti-symmetric stretching of CH2 (2921 cm-1) from PCA analysis. Nonetheless, the difference in tissue emissivity at room temperature was minimal, thus the contrast in the thermal image is low for intra-tissue classification. Besides, the thermal camera could only capture IR light between 3333-2000 cm-1. To record spectral data between 3900 - 900 cm-1 (mid-IR), Fourier transform infrared (FTIR) spectroscopic imaging was used to classify the different stages of colon disease. An automated processing framework was developed, that could achieve an overall classification accuracy of 92.7%. The processing steps included unsupervised k-means clustering of lipid bands, followed by Random Forest (RF) classification using the ‘fingerprint’ region of the data. The implementation of a correcting lens and the effect of the RMieS-EMSC correction on the tissue spectra were also investigated, which showed that computational RMieS-EMSC correction was more effective at removing spectral artefacts than the correcting lens. Furthermore, the effect of the fluctuations of surrounding humidity where the experiments were carried out was studied by using various supersaturated salt solutions. Significant peak changes of the phosphate band were observed, most notably the peak shift of the anti-symmetric stretching of phosphate bands from 1230 cm-1 to 1238 cm-1 was observed. By regulating and controlling humidity at its lowest, the classification accuracy of the colon specimens was improved without having to resort to alteration on the RF machine learning algorithm. In the ATR mode, additional apertures were introduced to the FTIR microscope, as a novel means of depth profiling the prostate tissue samples by changing the angle of incidence of IR light beam. Despite the successful attempts in capturing the qualitative information on the change of tissue morphology with the depth of penetration (dp), the spectral data were not suitable for further processing with machine learning as dp changes with wavelengths. Apart from the apertures, a ‘large-area’ germanium (Ge) crystal was introduced to enable simultaneous mapping and imaging of the colon tissue samples. Many advantages of this new implementation were observed, which included improvement in signal-to-noise ratio, uniform distribution, and no impression left on the sample. The research done in this thesis set a groundwork for clinical diagnosis and the novel implementations were transferable to studies of other samples.Open Acces

Metabolomics contributions to targeted and untargeted clinical analysis by chromatography and mass spectrometry

La investigación desarrollada en esta Tesis Doctoral se centró en realizar contribuciones en el ámbito del análisis clínico a través de estrategias metabolómicas tanto orientadas como globales en diferentes tipos de muestras biológicas mediante cromatografía de líquidos (LC) y espectrometría de masas (MS). Para ello primeramente se revisó exhaustiva y críticamente la bibliografía para conocer el estado del tratamiento de la orina como muestra y de los métodos de análisis de aire exhalado condensado, dos de los biofluidos utilizados en el desarrollo de esta Tesis. Además, se optimizaron métodos de análisis orientado para mejorar la cuantificación tanto de compuestos con potencial biomarcador como de fármacos y sus metabolitos para su aplicación en el diagnóstico y seguimiento de enfermedades o tratamientos. También se analizaron de forma global biofluidos para, (a) estudiar y optimizar el tratamiento de una muestra escasamente utilizada en el área clínica (el aire exhalado condensado –EBC), (b) identificar metabolitos con potencial predictivo para ayudar en el diagnóstico del cáncer de próstata utilizando la orina como muestra, y (c) mejorar y acelerar el tratamiento de datos a través de herramientas quimiométricas desarrolladas para combinar en una única matriz los datos obtenidos mediante ionización positiva y negativa en espectrometría de masas

Improving spatially resolved MSI analysis of tissue sections for DMPK and toxicity studies

The aim of the work presented herein was to re-evaluate the sample preparation pipeline for mass spectrometry imaging (MSI) experiments focusing on metabolite distributions and drug disposition. The work evaluated the steps from sample collection to quantitative interpretation of the results. A major focus of the work was set on the integration of the evaluated and newly developed workflows with orthogonal tissue imaging techniques. The work evaluated the effects of sample collection in formalin and subsequent preparation into formalin-fixed, paraffin embedded tissues. Overall, these treatments were found to substantially alter the tissue metabolome and distort metabolite and drug distributions, validating the current ‘gold standard’ of fresh-frozen tissues for metabolite and drug disposition focused MSI studies. These high-quality tissues require commonly cryo-sectioning to enable MSI analysis. Sample embedding strategies were explored to allow simultaneous preparation and analysis of several tissue specimens at once to increase technical reproducibility. To achieve highest preservation of the specimens, a novel embedding medium based on a hydroxypropyl-methylcellulose and polyvinylpyrrolidone hydrogel was developed. Within the frame of this work, strategies to decontaminate prepared tissue sections prior to MSI analysis will be reviewed, to minimize the infection risk when handling human tissues or specimen from infection models. Irradiation with UV-C light was found to be a suited decontamination as it enables accurate elucidation of endogenous biodistributions whilst only inflicting minor alterations to the tissue metabolome. The utility of a novel DESI setup based on a triple-quadrupole mass spectrometer was described and its application to elucidate drug disposition within tissues. The quantitative relationship of DESI- and MALDI-MSI were explored and some of the newly developed and established workflows were utilized in a multi-omics approach to elucidate the toxicokinetic effects of polymyxin B1 in a model of drug induced nephrotoxicity.Open Acces

Quantitative chemical imaging: A top-down systems pathology approach to predict colon cancer patient survival

Colon cancer is the second deadliest cancer, affecting the quality of life in older patients. Prognosis is useful in developing an informed disease management strategy, which can improve mortality as well as patient comfort. Morphometric assessment provides diagnosis, grade, and stage information. However, it is subjective, requires multi-step sample processing, and annotations by pathologists. In addition, morphometric techniques offer minimal molecular information that can be crucial in determining prognosis. The interaction of the tumor with its surrounding stroma, comprised of several biomolecular factors and cells is a critical determinant of the behavior of the disease. To evaluate this interaction objectively, we need biomolecular profiling in spatially specific context. In this work, we achieved this by analyzing tissue microarrays using infrared spectroscopic imaging. We developed supervised classification algorithms that were used to reliably segment colon tissue into histological components, including differentiation of normal and desmoplastic stroma. Thus, infrared spectroscopic imaging enabled us to map the stromal changes around the tumor. This supervised classification achieved >0.90 area under the curve of the receiver operating characteristic curve for pixel level classification. Using these maps, we sought to define evaluation criteria to assess the segmented colon images to determine prognosis. We measured the interaction of tumor with the surrounding stroma containing activated fibroblast in the form of mathematical functions that took into account the structure of tumor and the prevalence of reactive stroma. Using these functions, we found that the interaction effect of large tumor size in the presence of a high density of activated fibroblasts provided patients with worse outcome. The overall 6-year probability of survival in patient groups that were classified as “low-risk” was 0.73 whereas in patients that were “high-risk” was 0.54 at p-value <0.0003. Remarkably, the risk score defined in this work was independent of patient risk assessed by stage and grade of the tumor. Thus, objective evaluation of prognosis, which adds to the current clinical regimen, was achieved by a completely automated analysis of unstained patient tissue to determine the risk of 6-year death. In this work, we demonstrate that quantitative chemical imaging using infrared spectroscopic imaging is an effective method to measure tumor-tumor microenvironment interactions. As a top-down systems pathology approach, our work integrated morphometry based spatial constraints and biochemistry based stromal changes to identify markers that gave us mechanistic insights into the tumor behavior. Our work shows that while the tumor microenvironment changes are prognostic, an interaction model that takes into account both the extent of microenvironment modifications, as well as the tumor morphology, is a better predictor of prognosis. Finally, we also developed automated tumor grade determination using deep learning based infrared image analysis. Thus, the computational models developed in this work provide an objective, processing-free and automated way to predict tumor behavior

Sample Preparation in Metabolomics

Metabolomics is increasingly being used to explore the dynamic responses of living systems in biochemical research. The complexity of the metabolome is outstanding, requiring the use of complementary analytical platforms and methods for its quantitative or qualitative profiling. In alignment with the selected analytical approach and the study aim, sample collection and preparation are critical steps that must be carefully selected and optimized to generate high-quality metabolomic data. This book showcases some of the most recent developments in the field of sample preparation for metabolomics studies. Novel technologies presented include electromembrane extraction of polar metabolites from plasma samples and guidelines for the preparation of biospecimens for the analysis with high-resolution μ magic-angle spinning nuclear magnetic resonance (HR-μMAS NMR). In the following chapters, the spotlight is on sample preparation approaches that have been optimized for diverse bioanalytical applications, including the analysis of cell lines, bacteria, single spheroids, extracellular vesicles, human milk, plant natural products and forest trees